1078-0432.CCR-15-0737.Full-Text.Pdf

Author Manuscript Published OnlineFirst on September 18, 2015; DOI: 10.1158/1078-0432.CCR-15-0737 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer Distinct profiles for mitochondrial t-RNAs and small nucleolar

RNAs in locally invasive and metastatic colorectal cancer.

Lai Xu1, Joseph Ziegelbauer2, Rong Wang1, Wells W. Wu3, Rong-Fong Shen3, Hartmut

Juhl4, Yaqin Zhang1, Amy Rosenberg1* (corresponding author).

1OBP/DBRR-III, CDER, FDA, Silver Spring, Maryland, 2 HIV/AIDS Malignancy

Branch, NCI, Bethesda, Maryland, 3 Facility for Biotechnology Resources, CBER, FDA,

Silver Spring, Maryland, 4 Indivumed GMBH. Hamburg, Germany.

Running Title: Small RNAs as novel biomarkers in colorectal cancer

Keywords: MT-tRNAs, snoRNAs, microRNAs, mRNAs and tumor biomarkers

*Correspondence should be addressed to

Amy Rosenberg

Bldg 71, Rm 2238 10903 New Hampshire Ave Silver Spring, MD 20903 ([email protected])

Disclosure of Potential Conflicts of Interest

There are no current financial considerations to disclose.

Downloaded from clincancerres.aacrjournals.org on September 29, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on September 18, 2015; DOI: 10.1158/1078-0432.CCR-15-0737 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer Statement of translational relevance:

The precise molecular profiling of colorectal cancer (CRC) should facilitate development

of better staging systems as well as identify targets for development of novel treatments.

Our study focused principally on non-coding RNAs in the characterization of tumors at

high stage and with metastases. We identified mitochondrial transfer RNAs (MT-tRNAs)

as the top upregulated small RNAs (sRNAs) in high stage CRC and in CRC with defined

metastases. We also found that small nucleolar RNAs (snoRNAs) downregulated in

Prader-Willi syndrome (PWS), a metabolic disorder characterized by obesity (energy

storage), were commonly downregulated in CRC. These findings indicate that a hallmark

of tumor aggressiveness/progression pertains to energy storage and endowment of

mitochondria with enzymes critical for oxidative phosphorylation, despite a shift to

glycolytic metabolism in such tumors.

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer Abstract:

Purpose: To gain insight into factors involved in tumor progression and metastasis, we

examined the role of non-coding RNAs (ncRNAs) in the biological characteristics of

colorectal carcinoma (CRC), in paired samples of tumor together with normal mucosa

from the same CRC patient. The tumor and healthy tissues samples were collected and

stored under stringent conditions, thereby minimizing warm ischemic time.

Experimental Design: We focused particularly on distinctions among high stage tumors

and tumors with known metastases, performing RNA-Seq analysis which quantifies

transcript abundance and identifies novel transcripts.

Results: In comparing 35 CRCs, including 9 metastatic tumors (metastases to lymph

nodes and lymphatic vessels (LN/LV)), to their matched healthy control mucosa, we

found a distinct signature of MT-tRNAs and snoRNAs for metastatic and high stage

CRC. We also found the following: 1) MT-TF (phenylalanine) and snord12B expression

correlated with a substantial number of miRNAs and mRNAs in 14 CRCs examined; 2) a

miRNA signature of oxidative stress, hypoxia and a shift to glycolytic metabolism in 14

CRCs, regardless of grade and stage, and 3) heterogeneous MT-tRNA/snoRNA

fingerprints for 35 pairs.

Conclusions: These findings could potentially assist in more accurate and predictive

staging of CRC including identification of those CRC likely to metastasize.

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer Introduction

CRC is the third most common cancer and also the third leading cause of cancer-related

deaths in the United States (1). The prognosis in advanced cases is poor with more than

one-third of the patients dying from progressive disease within 5 years (2). However,

simultaneous evaluation of sRNA, miRNA and mRNA in matched pairs of CRC by

RNA-Seq, a technology for high throughput sequencing of the whole transcriptome with

some advantages over microarrays, has not been reported.

miRNAs are ncRNA molecules with 18-25 nucleotides (nts) in length and are known to

repress mRNAs by inhibiting translation and stimulating mRNA degradation (3).

Approximately 270 of such miRNAs have been shown to be dysregulated in CRC (3-5)

by real-time PCR and microarray. sRNAs are approximately 60-300 nts in length which

include transfer RNAs (tRNAs) and snoRNAs. MT-tRNAs, approximately 70-80 nts

long, are structurally distinct from nuclear-encoded tRNAs by having one less loop than

the nuclear canonical cloverleaf structure due to genome economization (6). Twenty-two

MT-tRNAs and 2 ribosomal RNAs (rRNAs) are required for the synthesis of 13 out of

the 84 essential protein subunits of NADH dehydrogenase, cytochrome reductase,

cytochrome oxidase and ATP synthase which are essential for ATP production through

oxidative phosphorylation in the mitochondria (7). Upregulated expression of such MT-

tRNAs has been found in breast cancer (8). snoRNAs play a crucial role in ribosome

biogenesis through methylation and pseudouridylation of rRNAs (9). A linkage between

snoRNAs and carcinogenesis has been established for non-small-cell lung cancer

(NSCLC) (10). mRNAs are RNA templates for protein synthesis and thus far, more than

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer a dozen mRNAs have been identified as biomarkers for staging and prognosis of CRC

(11).

Our study sought to investigate, using stringently collected and preserved tissue samples

(12) and RNA-Seq analysis, whether expression of unique sRNA species correlated with

and potentially contributed to the aggressive biological behavior of advanced stage

colorectal cancers and cancers with known metastasis, as compared to lower grade

tumors and healthy tissues.

Materials and Methods

Cohort

Thirty-five paired-tissues of pretreatment CRC were collected by Indivumed GmbH

(Germany). Histologically, tumor content is 50-70% in tumors and 0% in normal tissues.

Normal tissues were 5 cm away from tumors. Ischemia time to freeze was 6-11 minutes.

The normal mucosa was collected in a distal part of the bowel close to the resection

margins. Clinical and histopathological characteristics of the patients are summarized in

table S1.

Small RNA and miRNA sequencing

All 35 pairs were sequenced at small RNA level and first 15 pairs were also sequenced at

miRNA level. The RNA quality was assessed using the Agilent 2100 Bioanalyzer.

Samples with a RNA Integrity Number (RIN) of 7 or higher were processed to generate

libraries for small RNA sequencing following the Illumina®TruSeq™ Small RNA

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer Sample Preparation protocols. In brief, 3’ and 5’ RNA adapters, specifically modified to

target the 3’ hydroxyl group and 5’ phosphate group of most small RNA molecules, were

ligated to the ends of sRNAs present in 1 µg of high quality total RNA. Reverse

transcription was performed to generate the first-strand cDNAs followed by synthesis of

the second-strand cDNAs. The small RNA libraries were loaded onto a 4% agarose gel

after 11-cycle PCR amplification. DNA fragments of 145 to 160 bp containing miRNA

inserts were excised from the gel and purified for micro RNA sequencing (miRNA-Seq),

while those of 160 - 400 base pair (bp) containing longer inserts were similarly purified

for small RNA (including MT-tRNAs and snoRNAs) sequencing (sRNA-Seq). The yield

of small RNA library was quantified on Agilent 2100 Bioanalyzer. Twelve to twenty-

four small RNA libraries were pooled, denatured and loaded onto one lane of a flow cell

for cluster generation using the Illumina cBot. The flow cell was loaded onto Illumina

HiSeq 2500 sequencer (Illumina, San Diego, CA, USA) and subjected to single-end, 50-

and 100-cycle sequencing for miRNA and sRNA sequencing, respectively.

mRNA Sequencing

Fourteen pairs were further processed to generate mRNA-Seq libraries using Illumina

TruSeq “stranded mRNA sample prep kit.” In this method, poly (A) tailed RNA was

purified from 0.5 µg total RNA, fragmented and reverse-transcribed into cDNAs. Double

strand cDNAs were adenylated at the 3’ ends and ligated to indexed sequencing adaptors,

followed by limited-cycle (15) amplification. Paired-end sequencing was carried out on

HiSeq 2500 sequencer (Illumina, San Diego, CA, USA) for 100x2 cycles.

Sequencing Data Analysis

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer The miRNA-Seq data analysis was performed on NIH Biowulf supercomputer.

miRDeep2 was used to trim the adapter sequences, map the reads to the miRNA

database, miRBase (version19), and quantify miRNA expression levels. Novel miRNAs

were predicted using miRDeeps2 for aligning the reads against the reference human

genome (hg19). For mRNA sequencing, Tophat V.2.0.11 and Cufflinks V.2.2.1 were

used to align the reads in fastq files to the RefSeq UCSC human hg19 transcript reference

genome annotation database and the quantification of relative abundance of each

transcript was reported as Reads Per Kilo base per Million (RPKM). sRNA sequencing

data was analyzed using the software CLC Genomics Workbench 5 for the mapping of

trimmed reads to Ensemble GRCh37.75 noncoding RNA database and quantitation of the

sRNA expression levels. Statistical analyses and gene clustering were carried out using R

2.15.3 (The R Project for Statistical Computing Program). The sRNA expression levels

were normalized to the expression levels of all identified, known sRNAs. Paired Student

t-test was conducted to identify sRNAs with differential expression between cancers and

matched normal adjacent tissues (P < 0.05). Hierarchical clustering was conducted using

Euclidean distance to cluster RNAs and samples. By standard detection criteria (13),

5,171 sRNAs, 1009 miRNAs and 13580 mRNAs with 1 RPKM in at least one sample

were selected for analysis. Though paired t-test, 37 sRNAs, 204 miRNAs and 665

mRNAs were identified to have significant differential expression (p-value <0.05). The

expression patterns of sRNAs, miRNAs and mRNAs are presented on clustering analysis,

which were performed by the R package “gplots.” Principal component analysis (PCA)

was conducted to visualize differences between samples and cluster samples on the gene

expression profiles, with the majority of the data variation explained by PC1, PC2 and

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer PC3. Pearson correlation analysis was done with EXCEL 2003 and correlation cutoff was

0.6 (r>0.6). Plots were made with Prism 6 software and EXCEL 2003.

Verification of RNA-Seq fidelity and identification of colonic tissue house keeping

genes (Information S1)

Results

1) Identification of MT-tRNAs and snoRNAs as biomarkers for tumor with LN/LV

metastasis

To evaluate the sRNA profile of tumors that had documented lymph node (LN) and

lymphatic vessel (LV) metastases, the average ratios (Tumor/Normal: T/N) of sRNAs in

tumors with metastases (MET+) (9 tumors: T8LN, T11LV, T14LN, T15LN, T20LV, T25LV,

T26LN, T30LV and T35LN) (Table. S1) and tumors without documented metastases (MET-

) (26 tumors) were compared. We found that 6 MtRNAs (MT-TI (isoleucine), MT-TL

(leucine)1, MT-TE (glutamic acid), MT-TP (proline), MT-TF, MT-TL2 and 4 sRNAs

(snord19.2, snord86, snord77 and snora71D) were among the top 10 upregulated genes

with 2.5-6.5 fold increases in tumors with LN/LV metastasis compared to 1.5-3 fold

increases in tumors without LN/LV metastases (Fig. 1a). Next, we evaluated whether

such genes were also upregulated in advanced stage vs early stage tumors by comparing

the average ratios (T/N) of sRNAs among tumors at stage 1 (5 tumors), stage 2 (6

tumors) and stage 3 (21 tumors) compared to stage 4 (3 tumors). We found that there was

a 6-28 fold increased expression of 7 MT-tRNAs (MT-TC (cysteine), MT-TI, MT-TL1,

MT-TP, MT-TE, MT-TF and MT-TS (serine)1) and 3 snoRNAs (snord19.2, snord86 and

snord77) in stage 4 tumors compared to1-3 fold increases in all lower stage tumors (Fig.

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer 1b). Overall, MT-tRNAs were found to be highly expressed in high grade tumors and in

tumors with LN/LV metastases. Moreover, the upregulated expression of snord19.2,

snord86, snord77 and snora71D in CRC has not been reported and their potential roles in

malignant transformation/progression not known.

Thus to determine whether deregulated MT-tRNAs and snoRNAs can be used as a

signature to distinguish some metastatic and high stage tumors, unsupervised clustering

and PCA analysis of the selected 6 top upregulated MT-tRNAs (MT-TF, MT-TL1, MT-

TY (tyrosine), MT-TE, MT-TH (histidine), MT-TS1) and 2 top upregulated snoRNAs

(snord43, snord86) were performed. The hierarchical clustering distinguished MET+

T8LN, T14LN, T15LN, T25LV, T26LN, T30LV and T35LN (Fig. 1e) while the PCA plot

distinguished a subset of such MET+ tumors including T8LN, T14LN, T15LN and T26 LN

(Fig. 1g). Among 9 MET+ tumors, 3 (T20LV T30LV and T35LN) had only upregulation of

snord43 and snord86 but not MT-tRNAs. Furthermore, principal component analysis

(PCA) of 22 MT-tRNAs also distinguished metastatic T11 LV, T14LN, T15LN and T26LN

(Fig. S2a). Five MET- tumors (T9, T12, T13, T18, T22 and T23) that clustered with

MET+ tumors (Fig. 1e, 1g, Fig. S2a) were moderately differentiated stage 2 or 3 tumors

with high expression of MT-tRNAs (Fig. S3a). Thus, patients with tumors bearing such

MT-tRNA profiles may be at higher risk for progression and metastasis.

We next evaluated sRNAs that were most prominently downregulated in MET+ as well

as stage 4 tumors and found that the snord113-114-116 cluster, snord64, snord71,

snord93, snord107, scarna18, CTD2651B20.6 (CTD: comparative toxicogenomics

database) and AP000318.1 (AP: annotation process) were the 10 most downregulated

sRNAs with 1.2-10 fold decreases (Fig. 1c, 1d) Interestingly, the snord113-114-116

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer cluster, snord116-7 and snord64 are deleted in PWS, a severe metabolic disorder causing

excess adiposity which suggests a role for such dysregulation in energy storage in tumors

(14). However, these snoRNAs have not previously been specifically linked to CRC

malignant transformation or progression.

Since MET+ tumors clustered in multiple analyses of non-coding gene expression, we

performed an unsupervised clustering analysis of 14 tumor pairs using 11 known

metastatic mRNA biomarkers (15) including the following: liver-intestine cadherin 17

(CDH17), protocadherin 1 (PCDH1), transcription factor 5 (E2F5), matrix

metalloproteases 3 and 7 (MMP3 and MMP7), transcription factor AP-4 (TFAP4),

transcription factor 1 (E2F1), transforming growth factor, beta 1 (TGFB1), zinc finger

transcriptional repressor 1 (SNAI1) and extracellular matrix receptor III (CD44). The

resulting cluster analysis distinguished all MET+ tumors (T8LN, T14LN, T15LN and

T11LV) in 14 tumors (Fig. 1f).

Taken together, the coding RNA data support ncRNAs signatures that distinguish MET+

CRC. Thus, such MT-tRNA/snoRNA signatures have the potential to more precisely

identify these more aggressive and deadly tumors and suggest novel therapeutic targets.

2) Identification of sRNA as potential novel biomarkers for CRC

To evaluate the spectrum of sRNA biomarkers associated with tumors regardless of

grade, stage and LN/LV metastases, as distinguished from normal tissue, the average

ratios (T/N) of sRNAs of all 35 tumors and all 35 normal samples were compared. We

again found a predominance of 9 MT-tRNAs (MT-TI, MT-TL1, MT-TL2, MT-Y, MT-

TC, MT-TE, MT-TF, MT-TH and MT-TD) among the top 20 upregulated genes with

1.4-5 fold increases (Fig. S2b, Table. S3a). Nineteen snoRNAs and AP000318.1 were

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer among the 20 most downregulated genes in tumors with approximately 1.5-2.5 fold

decreases relative to normal tissue (Fig. S2c). Overall, 41 sRNAs were found up or

downregulated (Table. S2a, S2b, S2c, Fig. S3a, S3b, S3c). PCA analysis of these 32

differentially expressed snoRNAs distinguished 28 out of 35 tumors from normal

controls (Fig. S2d). Among 41 differentially expressed sRNAs (9 MT-tRNAs, 32

snoRNAs), we found that the upregulation of snord12B expression in tumors bore the

greatest statistically significant difference from normal samples (p value=3.830e-12)

(Fig. S4a). Consistent with upregulation of snord12B, the host gene of this snoRNA, zinc

finger nuclear transcription factor, x-box binding 1 anti-sense 1(ZNFX1-AS1) (16), a

long non-coding RNA, was upregulated in all examined CRCs (Fig. S4b). There was an

association strength (r=0.66) between the expression of snord12B and ZNFZ1-AS1 (Fig.

S4c). Paradoxically, the expression of ZNFZ1-AS1 is down-regulated in mouse

mammary tumors, and thus it may have distinct functions in different tissues or species.

Although, snord12B was reported as upregulated in rectal cancer (17), the profound

differences in snoRNA expression between CRC and normal mucosa have not hitherto

been reported and suggest potentially important roles for these ncRNAs in malignant

transformation, progression, or metastasis in CRC.

3) Correlation of sRNAs with miRNAs and mRNAs in primary CRCs

Key biological premises that underlie relationships of genes regarding “drivers’ and

‘passengers” consist of the following: highly co-expressed genes are more likely to be co-

regulated; and those genes that display prominent connectivity patterns tend to play

biologically influential or regulatory roles in disease-related processes (18-20). Measures

of node centrality in biological networks may detect genes with critical functional roles.

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer In gene co-expression networks, highly connected genes (i.e., candidate hubs) have been

associated with key drivers of disease pathways and gene connectivity has been shown to

be a measure of functional relevance (18-20). Thus to explore whether there were

potential cancer “driver” sRNAs, we performed correlational analyses among 41

differentially expressed sRNAs (9 MT-tRNAs, 32 snoRNAs), 204 miRNAs, and 665

mRNAs in 14 tumor pairs. Interestingly, we found that expression of some sRNAs, such

as MT-TF, snord12B and the snord114- cluster, correlated with expression of more than

100 to 450 miRNAs/mRNAs while expression of some sRNAs, such as MT-TY,

snord19B and snord83.9 correlated with expression of less then 10 miRNAs/mRNAs

(Table 1 and Information S2). Moreover, the upregulated expression of MT-TF and

snord12B sRNAs positively correlated with upregulated hypoxia, metastatic and pentose

phosphate pathway genes, such as mir21, mir181, ECE2, PHF19 and TKT, and

negatively correlated with expression of tumor suppressor genes, such as let7c, mir139,

CGRRF1 and SRSF5 (Fig. 2a, 2b, 2c, 2d). The downregulation of snord114-1 correlated

with downregulation of tumor suppressors, such as let7c and AHNAK (Information.

S2d). In addition, MT-TF, snord12B and snord114-1 were commonly deregulated in all

35 tumors (Table. S2a, S2b, S2c, Fig. S3a, S3b, S3c). Thus, our data suggest that

deregulation of these 3 sRNAs may be causally linked to tumorigenesis, tumor

proliferation or aggressiveness. The regulatory mechanisms controlling expression of

these 3 sRNAs are not known.

4) Evidence for Hypoxia and upregulation glucose metabolism in CRC

Since the prominent upregulation of MT-TF correlated with hypoxia biomarkers (mir21,

mir31) (Fig. 2a), we examined whether enzymes to mitigate the oxidative effects of

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer hypoxia were also affected. The glutathione anti-oxidative pathway consisting of 15

enzymes pertaining to glutathione S-transferase (GSTA), glutathione peroxidase (GPX),

gamma-glutamyltransferase (GGT), glutathione reductase (GSR),

microsomal/glutathione S-transferase (MGST) and catalase (CAT) in 14 CRCs were

examined and downregulation of 9 out of 15 anti-oxidative enzymes (Fig. 3a) was found

in all tumors as well as in 2 normal controls (N8, N14), which were associated with

MET+ tumors. Furthermore, we also examined hypoxia induced miRNAs (21, 22, 23)

and found 13 upregulated miRNAs pertaining to hypoxia in 14 out of 15 tumors (Fig.

3b), the sole exception being a poorly differentiated stage 3 tumor. Because IL-8 is

known to be upregulated and glycerol-3-phosphate dehydrogenase 1-like (GPD1L) is

known to be downregulated by hypoxia (24), we examined the mRNA expression profile

of these 2 genes in the 14 tumor pairs to establish the validity of the hypoxia clustering

analysis. Relative to healthy control tissue, IL-8 was upregulated 2-35 fold and GPD1L

was downregulated 1.2-5.2 fold in all tumors examined (Fig 3c). Thus, these data are

indicative of hypoxia in CRCs. Since hypoxia switches ATP production from oxidative

phosphorylation to glycolysis in tumor cells (25, 26, 27), we analyzed the expression of 3

SIRTs, which are known to suppress glycolysis (28), and found all tumors had down

regulation of all 3 SIRTs (Fig. 3d). Further, a clustering analysis of 6 genes directly

pertaining to glycolytic enzymes including glyceraldehyde-3-phosphate dehydrogenase

(GAPDH), glucose phosphate isomerase (GPI), pyruvate kinase2 (PKM2), enolase1

(ENO1), phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) and

phosphoglycerate kinase 1 (PGK1), clustered all 14 tumors together with upregulation of

all 6 genes, except in one stage 1 and two stage 3 tumors (Fig. 3e), while a clustering

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer analysis of 7 genes directly pertaining to TCA cycle enzymes, including malate

dehydrogenase 1 (MDH1), oxoglutarate (alpha-ketoglutarate) dehydrogenase (OGDH),

aconitase 2 (ACO2), succinate dehydrogenase (SDHD), citrate synthase (CS), fumarate

hydratase (FH) and isocitrate dehydrogenase 1 (IDH1), grouped 12 out 14 tumors

together (Fig. 3f). Among these 12 tumors, 6 high stage (3 or 4) tumors did not have

downregulation of all 7 TCA cycle enzymes. One poorly differentiated stage 3 tumor

(T4) and one poorly differentiated stage 4 tumor (T8) with LN metastases grouped with

normal samples in this analysis. These two tumors showed the most upregulated mRNA

expression of CS, a rate limiting enzyme in the TCA cycle, suggesting that while the

glycolytic pathway is generally upregulated and the TCA pathway is generally

downregulated in CRC, the most aggressive tumors may use both glycolysis and the TCA

cycle for energy production. Alternatively, one pathway or the other may predominate in

different cells within the heterogeneous tumor population or in different environmental

niches. Furthermore, all 14 examined tumors also had evidence of upregulation of the

pentose phosphate pathway as shown by upregulation of enzymes including transketolase

(TKT), glucose 6-phosphate dehydrogenase (G6PD) and 6-phosphogluconate

dehydrogenase (PGD), (Fig. 3g). The pentose phosphate pathway utilizes glucose to

generate ribonucleotides as well as NADPH and plays a pivotal role in anabolic processes

and in combating oxidative stress in glycolytic cancer cells. Overall, these data suggest

that there is increased utilization of glucose in CRCs via anaerobic glycolysis.

Intriguingly, one normal sample (N8), paired to MET+ T8, was adjacent to tumors in

various clustering analyses (Fig. 1f, 3a, 3d, 3f). Compared to other normal samples, this

N8 had higher expression of oncogenes such as SNAI1 and CD44 as well as lower

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer expression of anti-oxidative enzymes and tumor suppressors such as GST, GSR, CAT,

SIRT3 and SIRT6 (Fig. 1f, 3a, 3d) at the mRNA level. These data suggest the potential

presence of metastasis in this supposedly normal sample despite its designation as

“normal” by histological diagnosis (Table. S1).

5) sRNA profiles in 35 CRCs

Finally, to compare and contrast expression profiles in tumors across the histologic

spectrum, we established individual tumor sRNA fingerprints in paired matched samples

and evaluated commonality and dissimilarity in sRNA expression. Since both the

magnitude of the ratio (T/N) and of the subtractive difference (T-N) in gene expression

may have biological significance, we established both relative ratio (T/N) and absolute

difference (T-N) fingerprints for each tumor. We then selected the three most highly up

and down regulated sRNAs based on values of (T/N) or (T-N) to build a sRNA

fingerprint for each tumor. Relative ratio fingerprints identified the MT-tRNA family,

CTD 2651B20.6 and snord19 cluster as the most frequently upregulated genes while the

snord114 cluster, AP000318.1 and snord64 were identified as the most frequently down

regulated genes (Table. 2a, Table. S3a), largely reflecting earlier analyses (Fig. S2b,

S2c). As in the initial analysis of average fold increased or decreased expression of each

sRNA in all MET+ tumors (Fig. 1a, 1c), the 3 most upregulated (8-20X) genes in 9

individual metastatic tumors were also either MT-tRNAs or the snord19 family while the

3 most downregulated (2-25X) genes again were in the snord113/114/116 cluster (Table.

S3a). For absolute difference fingerprints, we found that in metastatic tumors, snord43,

MT-tRNAs and the snord12 cluster were the most frequently upregulated genes while

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer snord71, snord59A and the snord114 cluster were the most frequently down regulated

genes among the top 3 up/downregulated genes (Table. 2b, Table. S3b). Met+ tumors

(T8LN, T15LN, T25LV and T35LN) displayed the greatest level of absolute RPKM changes

of snord43, snord12 cluster and snord113/114/116 cluster expression. The deregulation of

these snoRNAs in CRC has thus far not been reported and their roles, if any, in

tumorigenesis, proliferation, or metastasis are not known. Overall, each tumor had its

own genomic signature, with some unique features and some common features. Based on

the presence or absence of MT-tRNAs in the top 3 upregulated genes, 35 CRCs can be

placed into two groups, regardless of their histological types and TNM classification

(Table 2c).

Discussion

CRC is a disease with variegated genetic and epigenetic profiles (29). Thus advanced

molecular profiling of CRC may improve staging and grading of tumors and thus

predicting clinical course, but as well in defining specific tumor survival and proliferation

pathways and thereby elucidate novel drug treatments. Currently there are a number of

drugs that target such specific oncogenic pathways (30). In this study, we delineated

sRNA for 35 CRCs. We found that all 35 CRCs had distinct combinations of common as

well as uniquely deregulated genes. This new understanding of oncogenic mechanisms

has begun to influence risk assessment, diagnostic categories, and therapeutic strategies,

with increasing use of drugs and antibodies designed to counter the influence of specific

molecular drivers.

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer Our study mainly focused on distinctions among tumors of high stage and tumors with

metastases. In this study, we identified MT-tRNAs as the top upregulated sRNAs in

LN/LV positive and high stage CRC, as well as a signature of sRNAs associated with

such tumors and, in some cases, with their adjacent “normal” tissues which likely contain

undetected metastasis. Furthermore, the distinct profiles of tumors with metastasis, as

defined by both ncRNA and coding RNA signatures in CRC reveals some novel

elements. Especially notable was the stunning downregulation of the snord113/114/116

clusters which are deleted in PWS, a genetically imprinted disease marked by profound

obesity which may likewise enhance energy storage in cancer cells, but not described

thus far in cancer. Whether these snoRNA clusters having tumor suppressor function

need to be studied.

A multitude of data support the role of hypoxia in tumor physiology as hypoxia has been

shown to upregulate glycolytic metabolism and MT-tRNAs (31) and downregulate

enzymes involved in the TCA cycle. More generally, hypoxia has long been recognized

to play a role in promoting the invasive and metastatic behavior of cancer cells and their

ability to disseminate from established colonies and establish new colonies in distinct

environments may depend on their ability to diversify metabolic pathways (21, 23). In

this regard, the stringency of tumor harvest and preservation in our study is critical in

assuring that these findings are not artifacts of prolonged ex-vivo time duration prior to

storage (12). The superposition of upregulated MT-tRNAs, which may support

generation of ATP through mitochondrial oxidative phosphorylation, on a profile of a

shift to glycolytic metabolism, suggests that tumors with enhanced capacity for local and

distant invasion are poised to use either oxidative phosphorylation or glycolytic

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer metabolism, depending on the oxygenation circumstances in which they find themselves.

Though downregulation of snoRNAs such as snord50A, h5sn2, snord43 and snord44,

have been found in breast, prostate, brain and lung cancers (32, 33), the panel of

downregulated snoRNAs, identified in this study, have not been reported previously and

may be unique to colonic cancers. Interestingly, the host gene of the downregulated

snord114 cluster (Fig. S5a) is the maternally expressed 3 RNA gene, MEG3 (34, 35).

MEG3 inhibits cancer cell proliferation by both p53-dependent and p53 independent

pathways (34, 35). Consistent with this finding, our sRNA/mRNA sequencing data

revealed a strong expression correlation (downregulation of expression) between

snord114-23/snord114-26 and MEG3 (rs>0.8) (Fig. S5b). Whether these imprinted

snoRNAs have specific biological functions distinct from their host gene in malignant

transformation needs to be further evaluated.

In conclusion, the 35 paired samples analyzed in this study serve as a training set that

helps to identify sRNA signatures for metastatic and advanced CRC. To strengthen our

findings, these putative sRNA markers will be evaluated and potentially validated by an

independent study with larger numbers of samples. The hope is that such ncRNAs and

coding RNAs will be validated as biomarkers and therapeutic targets which will

ultimately benefit patients.

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer Ethics Statement

Written informed consent was obtained from all the participants involved in the study via

the procuring laboratory at Indivumed (Hamburg, Germany).

Acknowledgments

The authors would like to thank Julia Berkson, Baolin Zhang and Ashutosh Rao for their

critical review and comments on this manuscript.

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Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer

Figure legends:

Fig. 1. Identification of 10 most up/down regulated sRNAs between tumors with

metastasis (Met+) and without metastasis (Met-). All 35 tumors were grouped into Met+

and Met-, T/N ratios were obtained, and log2 ratios of LN positive /negative ratio were

sorted from high to low and plotted. (a) Six MT-tRNAs were among top 10 expressed

sRNAs. The difference in the average ratio of the top 10 sRNAs expressed between Met+

and Met- tumors was highly significant, (P=0.00685 paired t-test). (b) Identification of

most up regulated sRNAs by tumor stages. Tumors were grouped according to stage (T1,

T2, T3 and T4) and the log2 ratios (T1/N1, T2/N2, T3/N3 and T4/N4) were obtained and

plotted. Expression in stage 4 tumors differed significantly from all other stages: stage 1

(P<0.0001). There were no differences among stages 1, 2 and 3 (P>0.06) (ANOVA). (c)

10 snoRNAs were among the 10 most down regulated sRNAs in LN/LV positive tumors

but not statistically significant (P=0.364) by student t- test. (d) Nine snoRNAs were

among the 10 most downregulated sRNAs in stage 4 tumors. Stage 4 differed

significantly from stage1/2/3 (P<0.0458) and stage1/2/3 had no significant difference

among them (P>0.22) by ANOVA. (c) and (d) were plotted as –log2 ratio. (e)

Hierarchical unsupervised clustering analysis of 6 MT-tRNAs/2 snoRNA expression

RPKMs in 35 CRC pairs. Met+ tumors tended to cluster at higher expression side of the

map. (f) Hierarchical unsupervised clustering analysis of mRNA expression of 11

markers associated with metastatic tumors by RPKMs in 14 CRC pairs. This 11-coding

gene signature grouped T14/T15 together and separated all tumors from normal samples.

Red arrows indicate Met+ tumor samples. Red and green bars delineate tumors from

normal controls. Red in the heatmap indicates an expression level above the mean in all

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer samples and blue indicates expression levels lower than the mean of all samples. (g) 3D

PCA analysis of 6 MT-tRNAs/2 snoRNA (same sRNAs as in Fig. 1e) in 35 CRC pairs.

Four Met+ tumors (T8LN, T14LN, T15LN, T26LN) (boxed) and 2 Met- tumors (T12,

T13) were clearly separately from remaining tumors and normal controls.

Fig. 2. (a) Nine upregulated and 1 downregulated miRNAs correlated with MT-TF. (b)

Five top positive and negative correlated miRNAs with snord12B. Five top positive and

negative correlated mRNAs for MT-TF (c) and snord12B (d). This figure was

summarized from Information S2. Positive correlated genes are represented by brown

oval dots and negative correlated genes are represented by blue oval dots. The correlation

coefficient value for each gene pairs was showed and bigger dots had stronger

correlation.

Fig. 3. (a) Hierarchical unsupervised clustering analysis of the selected 16 anti-oxidative

stress enzymes in 14 CRC pairs. All tumors and 2 normal controls (N8, N14) had

downregulation of anti-oxidative stress pathway. (b) Hierarchical unsupervised clustering

analysis of the selected 13 hypoxia induced upregulated miRNAs in 15 CRC pairs. Only

one tumor (T5) was grouped with normal samples. (c) mRNA expression profiles of

hypoxia upregulated IL8 (p=0.005) and hypoxia downregulated GPD1L (paired t-test:

p=2.6-07E) in 14 CRC pairs. (d) Hierarchical unsupervised clustering analysis of 3 SIRT

mRNAs in 14 CRC pairs. All tumors had lower mRNA levels of 3 SIRT and were

separated from normal controls. (e) Hierarchical unsupervised clustering analysis of the 6

glycolytic enzyme mRNAs in 14 CRC pairs. All tumors had higher mRNA levels of

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer glycolytic genes and were separated from normal controls. (f) Hierarchical unsupervised

clustering analysis of the 7 TCA cycle mRNAs in 14 CRC pairs. Twelve of 14 tumors

had lower mRNA levels of TCA cycle genes were separated from normal controls. (g)

Hierarchical unsupervised clustering analysis of the 3 pentose phosphate pathway (PPP)

mRNAs in 14 CRC pairs. All tumors had high mRNA levels of 3 PPP enzymes and were

separated from normal controls. Red and green bars delineate tumors and normal

controls, respectively. Red in the heatmap indicates an expression level above the mean

in all samples and blue indicates expression levels lower than the mean of all samples.

The clustering analysis was based genes’ RPKMs.

Table. 1. Pearson’s correlation test was performed on RPKM value of each gene group

cross 14 pairs. All genes showing correlation coefficient (r)> 0.6 with MT-tRNAs and

snoRNAs (Information S2) were summarized in this table.

Table. 2. (a) The frequency of deregulated sRNAs in all 35 CRCs based on T/N ratio

fingerprint. Gene ratios of each tumor pair (T/N) were calculated and sorted from high to

low. Three top upregulated genes and 3 top downregulated genes were selected from each

tumor to build tumor fingerprints. The actual ratios of each gene were shown in Table.

S3a. (b) Frequency of deregulated sRNAs in 35 CRCs based on subtractive (T-N) RPKM

fingerprint. The differences of each tumor pair (T-N) were calculated and sorted from

high to low. Three top upregulated and downregulated genes were selected from each

tumors and actual differential RPKM were shown in Table. S3b. Commonly deregulated

genes presented in more than once in 35 CRCs while uniquely deregulated genes just

presented only once in 35 CRCs. (c) Classification of CRCs based on the presence of

Non-coding small RNAs as biomarkers for high stage and locally metastatic colorectal cancer MT-tRNAs in top 3 upregulated genes according to both ratio and subtractive

fingerprints.

Comparison of upregulated sRNAs between MET+ and MET- tumors Comparison of upregulated sRNAs among different stages of tumor 7 st1 st2 st3 st4 MT-TC 6 6 5 MT-TI MET- 5 4 MT-TL1 T/N 3 MET+ 4 MT-TP 2 T/N 2 3 1 SNORD19.2 0 log 2 MT-TE 1 SNORD86 MT-TI MT-TE MT-TP MT-TF MT-TL1 MT-TL2 0 SNORD86 SNORD77 MT-TF SNORD19.2 SNORA71D T1/N1 T2/N2 T3/N3 T4/N4 MT-TS1 10 most upregulated sRNAs tumor stage SNORD77

Fig. 1a Fig. 1b

Comparison of downregulated sRNAs between MET+ and MET- Comparison of downregulated sRNAs among different stages of tumors tumor

st1 st2 st3 st4 SNORD114-3 1 4 0.8 3.5 AP000318.1 0.6 MET- 3 SNORD71

T/N 0.4 MET+ 2.5 SNORD83.9 0.2

T/N 2 2 0 1.5 SNORD114-1

-log 1 SCARNA18 0.5 SNORD107 SNORD64 0 SNORD125 SCARNA18SNORD107 SNORD113-9 SNORD114-3SNORD114-1SNORD116-7 SNORD116-29 SNORD114-14 SNORD116-29 -0.5 T1/N1 T2/N2 T3/N3 T4/N4 SNORD116-7 10 most downregulated sRNAs tumor stage SNORD64

Fig. 1c Fig. 1d

Met+ Met+ Met+ Met+ Fig. 1e

Fig. 1f Met+ Met+

Fig. 1g

Pearson correlation analysis (CA) of sRNAs, miRNAs and mRNAs in 14 primary tumor pairs

Differentially expressed 9 MT-tRNAs CA Differentially expressed 32 snoRNAs

CA CA

Differentially expressed 204 miRNAs Differentially expressed 665 mRNAs

Table. 1 The number of co-expressed miRNA/mRNA with sRNAs

Correlated Correlated Correlated Correlated Correlated Correlated Correlated

MT-tRNA snoRNA # miRNA # mRNA # snoRNAs miRNA # mRNA # snoRNAs miRNA # mRNA #

MT-TF 1 10 151 SNORD12B 27 451 SNORD114-1 31 80

MT-TC 0 3 19 SNORD12C 16 184 SNORD114-23 11 29

MT-TL1 0 2 10 SNORD12 18 90 SNORD114-26 13 25

MT-TS1 0 1 2 SNORD78 13 60 SNORD107 13 23

MT-TI 0 2 2 SNORD77 12 53 VTRNA1 5 10

MT-TE 1 5 1 SNORD125 8 35 SNORD114-14 3 8

MT-TY 0 5 0 SNORA84 12 12 SNORD114-25 4 7

MT-TH 0 2 0 SNORD71 11 10 SNORD64 12 3

MT-TL2 0 7 0 SNORD1A 10 1 SNORD116-29 0 3

SNORD19B 6 0 SNORD113 13 1

SCARNA18 9 0 SNORA71D 5 1

SNORD54 5 0 SNORD93 13 0

SNORD59A 2 0 SNORD83.9 8 0

CTD-2615B 0 0 AP000318.1 0 0

snord1A 0 0 SNORD116-7 0 0

snord86 0 0 snord43 0 0

Correlation between MT-TF and miRNAs

mir21 (oncomir/hypoxia) Let7c (tumor suppressor) r=0.71 r=-0.63

mir660 (?)

r=0.71 mir877 (?)

r=0.62 mir31 (oncomir/hypoxia) MT-TF

r=0.68 mir25 (oncomir/motility)

mir455 (oncomir) r=0.62

r=0.68

mir103 (oncomir/metastases) mir103a (oncomir/metastases) r=0.67 r=0.67

Fig. 2a

Correlation between snord12B and miRNAs

mir181d (cancer progression) r=0.78 mir375 (tumor suppressor) r=-0.71 mir452 (oncomir)

r=0.77 mir378i (tumor suppressor) r=-0.71

mir222 (oncomir) mir378a (tumor suppressor) snord12B r=0.77 r=-0.72

mir497 (tumor suppressor) mir454 (cell proliferation) r=-0.74 r=0.71

mir139 (tumor suppressor) mir24-2 (invasion ) r=-0.78 r=0.69

Fig. 2b

Correlation between MT-TF and mRNAs

ECE2 (metastases) SIPA1l2 (?) r=0.71 r=-0.64

DKC1 (metastases) ASAP3 (anti-invasion) r=0.68 r=-0.65

NLE1 (embryogenesis) CGRRF1(tumor suppressor) r=0.67 MT-TF r=-0.67

SCAMP3 (carries) TSHZ1 (zinc finger protein) r=-0.68 r=0.65

TKT (pentose phosphate pathways ) ZNF540 (zinc finger protein) r=0.65 r=-0.68

Fig. 2c

Correlation between snord12B and mRNAs

RRP9 (ribosome biogenesis) r=0.82 VTI1B (vesicle transport) r=-0.77 ZMYND19(zinc finger protein)

r=0.77 XYLT1 (xylosytransferase) r=-0.68

PHF19 (oncogene) snord12B MBOAT1 (acyltransferase) r=0.77 r=-0.78

SRSF5 (tumor suppressor) PDCD2L (programmed cell death) r=-0.78 r=0.75

CCDC85C (coiled protein) NOB1 (oncogene) Fig. 2d r=-0.79 r=0.73

Fig. 3a glutathione pathway low high

Fig. 3b

hypoxia induced miRNAs low high

IL8 GPD1L

700 70 600 60 500 50 400 T 40 T

300 30 N RPKM RPKM N 200 20 100 10 0 0 T1 T2 T3 T10 T13 T11 T12 T9 T15 T4 T5 T14 T6 T8 T1 T2 T3 T10 T13 T11 T12 T9 T15 T4 T5 T14 T6 T8 tumor pairs tumor pairs Fig. 3c W M P W M P Downloaded from clincancerres.aacrjournals.org on September 29, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on September 18, 2015; DOI: 10.1158/1078-0432.CCR-15-0737 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Fig. 3d SIRTs down up

glycolysis pathway Fig. 3e down up

Fig. 3f down up TCA cycle

Fig. 3g pentose phosphate pathway down up

Table. 2a Frequency of sRNAs/miRNA in 3 top up/downregulated genes in T/N ratio fingerprint

13 top upregulated genes in 35 CRCs (T/N: 1.2 to 20X 14 top downregulated genes in 35 CRCs (N/T: 1.4 to 50X folds) Total: 105/35 folds) Total :105/35

MT-tRNA family 45/35 snord113-114-116 cluster 45/35 snord19 cluster 16/35 AP000318.1 16/15

CTD2651B20.6 13/35 snord64 12/35 snord77 8/35 snord107 9/15

snord12 cluster 7/35 VTRNA1 6/15

snora71D 6/35 scarna18 4/15

snord78 4/35 snord49B 3/15

snord98 1/35 snord123 3/15

snord49A 1/35 mir3607 2/15

snord15A 1/35 snord71 1/15

RNY4 1/35 snord125 1/15

snord101 1/15 snord93 1/15

snord1A 1/35 snord65 1/35

snord28 1/35

Table. 2b Frequency of sRNAs in 3 top up/downregulated genes in T-N subtractive RPKM fingerprint

5 upregulated sRNAs (T-N=0.5 to 15 x 106 RPKM)

T/N ratio profile Frequency (total: 105/35) snord12 cluster 1.5-4 fold 71/35 snord43 1.5-2 folds 16/35

MT-tRNAs 1.5-10 folds 16/35 snord19.2 3 fold 1/35 snord78 2 fold 1/35

5 downregulated sRNAs (N-T=0.2 to 2 x 106 RPKM)

T/N ratio profile Frequency (total: 105/35) snord71 1.5-4 folds 33/35 snord59A 1.5-2 folds 33/35 snord114 cluster 1.5-4 folds 20/35

AP0000318.1 1.5-4 folds 18/35 snord107 1.5-4 folds 1/35

Table. 2c CRC subtypes based on both ratio and subtractive fingerprints

Type I Type II snoRNAs only MT-tRNAs + snoRNAs T5, T16, T19, T1, T2, T3, T7,T10, T13, T11, T12, T9, T15, T4, T20, T22,T27, T14, T6, T8, T17, T18, T21, T23, T24, T25, T30, T31, T35 T26, T28, T29, T32, T33, T34

Distinct profiles for mitochondrial t-RNAs and small nucleolar RNAs in locally invasive and metastatic colorectal cancer.

Lai Xu, Joseph Ziegelbauer, Rong Wang, et al.

Clin Cancer Res Published OnlineFirst September 18, 2015.

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